AI Manufactured Housing Fair Lending Review Playbook

A bank's manufactured housing portfolio has 1,840 loans originated in the past 18 months. Manufactured housing borrowers are disproportionately low-income and rural. The bank's manufactured housing loan terms (rates, fees, LTV limits) are materially less favorable than its site-built mortgage terms. An HMDA analysis flags demographic differences between the two portfolios.

When to use this playbook

• Use this playbook when the decision looks like the situation above: A bank's manufactured housing portfolio has 1,840 loans originated in the past 18 months.
• It is a fit when you have source files in hand and need a structured, reviewable analysis — not a generic chat answer about "Manufactured Housing Fair Lending Review".
• Do not use it as a substitute for licensed, legal, clinical, or authorized official judgment in the domain.

What you'll need

• Manufactured housing loan file (1,840 loans, 18 months)
• Site-built mortgage portfolio for comparison
• Borrower demographics and geographic data
• Loan terms comparison: rate, fee, LTV, DTI limits, loan type
• HMDA LAR showing demographic composition of each portfolio

Attachments: Spreadsheets (Spreadsheets)

You are a fair lending analyst reviewing manufactured housing lending practices at a bank. I am attaching:

Work only from the attached source files. If a conclusion is not supported, say so.

Produce:
1. Quantify the term disparity between manufactured housing and site-built loans: rate differential, fee differential, and LTV limit gap.
2. Assess whether the manufactured housing borrower population is disproportionately minority, low-income, or rural compared to the site-built population—and whether that demographic difference is driving the term disparity.
3. Determine whether the less favorable manufactured housing terms are justified by legitimate credit risk differences (collateral value, default rates) or represent a facially neutral policy with disparate impact.
4. Assess the ECOA and FHA exposure if the disparate impact is not justified by business necessity.
5. Tell me what policy changes would address the disparate impact and how to document the business necessity defense for the terms that are legitimately different.

Call out where independent models are likely to disagree, and list follow-up documents a reviewer should request.

What to expect

• Term disparity quantification (rate, fee, LTV)
• Portfolio demographic comparison
• Business necessity analysis for term differences
• ECOA/FHA exposure assessment
• Policy change recommendations and business necessity documentation plan

Review before you act

• Validate this output against source files before relying on it: Quantify the term disparity between manufactured housing and site-built loans: rate differential, fee differential, and LTV limit gap.
• Validate this output against source files before relying on it: Assess whether the manufactured housing borrower population is disproportionately minority, low-income, or rural compared to the site-built population—and whether that demographic difference is driving the term disparity.
• Validate this output against source files before relying on it: Determine whether the less favorable manufactured housing terms are justified by legitimate credit risk differences (collateral value, default rates) or represent a facially neutral policy with disparate impact.
• Validate this output against source files before relying on it: Assess the ECOA and FHA exposure if the disparate impact is not justified by business necessity.
• Confirm every cited figure, date, counterparty, or requirement against the attached originals — models compress and can drop a qualifier.
• Treat disagreement between models as a review item, especially on classification, materiality, and recommended next action.
• Do not authorize an operational, clinical, legal, credit, or enforcement action solely because the models agree.